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Mastering Concurrency in Python
Mastering Concurrency in Python

Mastering Concurrency in Python: Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

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Mastering Concurrency in Python

Amdahl's Law

Often used in discussions revolving around concurrent programs, Amdahl's Law explains the theoretical speedup of the execution of a program that can be expected when using concurrency. In this chapter, we will discuss the concept of Amdahl's Law, and we will analyze its formula, which estimates the potential speedup of a program and replicates it in Python code. This chapter will also briefly cover the relationship between Amdahl's Law and the law of diminishing returns.

The following topics will be covered in this chapter:

  • Amdahl's Law
  • Amdahl's Law: its formula and interpretation
  • The relationship between Amdahl's Law and the law of diminishing returns
  • Simulation in Python, and the practical applications of Amdahl's Law

Technical requirements

Amdahl's Law

How do you find a balance between parallelizing a sequential program (by increasing the number of processors) and optimizing the execution speed of the sequential program itself? For example, which is the better option: Having four processors running a given program for 40% of its execution, or using only two processors executing the same program, but for twice as long? This type of trade-off, which is commonly found in concurrent programming, can be strategically analyzed and answered by applying Amdahl's Law.

Additionally, while concurrency and parallelism can be a powerful tool that provides significant improvements in program execution time, they are not a silver bullet that can speed up any non-sequential architecture infinitely and unconditionally. It is therefore important for developers and programmers to know and understand the limits of the speed...

Formula and interpretation

Before we get into the formula for Amdahl's Law and its implications, let's explore the concept of speedup, through some brief analysis. Let's assume that there are N workers working on a given job that is fully parallelizable—that is, the job can be perfectly divided into N equal sections. This means that N workers working together to complete the job will only take 1/N of the time it takes one worker to complete the same job.

However, most computer programs are not 100% parallelizable: some parts of a program might be inherently sequential, while others are broken up into parallel tasks.

The formula for Amdahl's Law

Now, let B denote the fraction of the program that is...

Amdahl's Law's relationship to the law of diminishing returns

Amdahl's Law is often conflated with the law of diminishing returns, which is a rather popular concept in economics. However, the law of diminishing returns is only a special case of applying Amdahl's Law, depending on the order of improvement. If the order of separate tasks in the program is chosen to be improved in an optimal way, a monotonically decreasing improvement in execution time will be observed, demonstrating diminishing returns. An optimal method indicates first applying those improvements that will result in the greatest speedups, and leaving those improvements yielding smaller speedups for later.

Now, if we were to reverse this sequence for choosing resources, in which we improve less optimal components of our program before more optimal components, the speedup achieved through the...

How to simulate in Python

In this section, we will look at the results of Amdahl's Law through a Python program. Still considering the task of determining whether an integer is a prime number, as discussed in Chapter 1, Advanced Introduction to Concurrent and Parallel Programming, we will see what actual speedup is achieved through concurrency. If you already have the code for the book downloaded from the GitHub page, we are looking at the Chapter02/example1.py file.

As a refresher, the function that checks for prime numbers is as follows:

# Chapter02/example1.py

from math import sqrt

def is_prime(x):
if x < 2:
return False

if x == 2:
return x

if x % 2 == 0:
return False

limit = int(sqrt(x)) + 1
for i in range(3, limit, 2):
if x % i == 0:
return False

return x

The next part of the code is a function that takes in an...

Practical applications of Amdahl's Law

As we have discussed, by analyzing the sequential and parallelizable portion of a given program or system with Amdahl's Law, we can determine, or at least estimate, the upper limit of any potential improvements in speed resulting from parallel computing. Upon obtaining this estimation, we can then make an informed decision on whether an improved execution time is worth an increase in processing power.

From our examples, we can see that Amdahl's Law is applied when you have a concurrent program that is a mixture of both sequentially and executed-in-parallels instructions. By performing analysis using Amdahl's Law, we can determine the speedup through each incrementation of the number of cores available to perform the execution, as well as how close that incrementation is to helping the program achieve the best possible...

Summary

Amdahl's Law offers us a method to estimate the potential speedup in execution time of a task that we can expect from a system when its resources are improved. It illustrates that, as the resources of the system are improved, so is the execution time. However, the differential speedup when incrementing the resources strictly decreases, and the throughput speedup is limited by the sequential overhead of its program.

You also saw that in specific situations (namely, when only the number of processors increases), Amdahl's Law resembles the law of diminishing returns. Specifically, as the number of processors increases, the efficiency gained through the improvement decreases, and the speedup curve flattens out.

Lastly, this chapter showed that improvement through concurrency and parallelism is not always desirable, and detailed specifications are needed for an effective...

Questions

  • What is Amdahl's Law? What problem does Amdahl's Law try to solve?
  • Explain the formula of Amdahl's Law, along with its components.
  • According to Amdahl's Law, will speedup increase indefinitely as the resources of the system improve?
  • What is the relationship between Amdahl's Law and the law of diminishing returns?

Further reading

For more information you can refer to the following links:

  • Amdahl's Law (https://home.wlu.edu/~whaleyt/classes/parallel/topics/amdahl.html), by Aaron Michalove
  • Uses and abuses of Amdahl's Law, Journal of Computing Sciences in Colleges 17.2 (2001): 288-293, S. Krishnaprasad
  • Learning Concurrency in Python: Build highly efficient, robust, and concurrent applications (2017), Elliot Forbes
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Key benefits

  • Explore the core syntaxes, language features and modern patterns of concurrency in Python
  • Understand how to use concurrency to keep data consistent and applications responsive
  • Utilize application scaffolding to design highly-scalable programs

Description

Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language

Who is this book for?

This book is for developers who wish to build high-performance applications and learn about signle-core, multicore programming or distributed concurrency. Some experience with Python programming language is assumed.

What you will learn

  • Explore the concepts of concurrency in programming
  • Explore the core syntax and features that enable concurrency in Python
  • Understand the correct way to implement concurrency
  • Abstract methods to keep the data consistent in your program
  • Analyze problems commonly faced in concurrent programming
  • Use application scaffolding to design highly-scalable programs
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Table of Contents

21 Chapters
Advanced Introduction to Concurrent and Parallel Programming Chevron down icon Chevron up icon
Amdahl's Law Chevron down icon Chevron up icon
Working with Threads in Python Chevron down icon Chevron up icon
Using the with Statement in Threads Chevron down icon Chevron up icon
Concurrent Web Requests Chevron down icon Chevron up icon
Working with Processes in Python Chevron down icon Chevron up icon
Reduction Operators in Processes Chevron down icon Chevron up icon
Concurrent Image Processing Chevron down icon Chevron up icon
Introduction to Asynchronous Programming Chevron down icon Chevron up icon
Implementing Asynchronous Programming in Python Chevron down icon Chevron up icon
Building Communication Channels with asyncio Chevron down icon Chevron up icon
Deadlocks Chevron down icon Chevron up icon
Starvation Chevron down icon Chevron up icon
Race Conditions Chevron down icon Chevron up icon
The Global Interpreter Lock Chevron down icon Chevron up icon
Designing Lock-Based and Mutex-Free Concurrent Data Structures Chevron down icon Chevron up icon
Memory Models and Operations on Atomic Types Chevron down icon Chevron up icon
Building a Server from Scratch Chevron down icon Chevron up icon
Testing, Debugging, and Scheduling Concurrent Applications Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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The english is awful, which makes me concerned about the correctness of the content. It feels like the book was copy-pasted from various sources from the Internet.
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